Impact of Assimilating Radar Reflectivity Data using Novel Parameterized Forward Operators on Convective-Scale Short-term Forecasts

Over the past few decades, the assimilation of radar reflectivity data into storm-scale models has led to impressive achievements in updating the dynamical and hydrometeor information for the model’s initial fields to improve convective-scale short-term forecasts. A radar forward operator linking the model state variables to radar observations plays a key role in the assimilation of radar reflectivity data. In most current radar reflectivity assimilation studies and operational systems, forward operators based on the Rayleigh scattering approximation and ignoring the melting process fall short of accurately characterizing the reflectivity of large and/or mixed-phase particles. In this study, we assess the impact of assimilating reflectivity data using newly developed parameterized forward operators (PFO) with a continuous melting model in a 3DVAR framework. Two distinct sets of cycled assimilation and forecast experiments are conducted for eight high-impact severe convective weather events from the Hazardous Weather Testbed (HWT) 2019 Spring Experiments. One set of experiments (ExpRFO) uses the aforementioned commonly used reflectivity forward operator based on the Rayleigh scattering (RFO) with hydrometeor classification, while the other uses the PFO (ExpPFO_New) for radar reflectivity with a new continuous melting model. The analysis and forecast results are first examined in detail for a classic tornadic supercell case on 24 May 2019, with the potential benefits provided by parameterized reflectivity forward operators then evaluated for all eight cases. It is demonstrated that ExpPFO_New provides more robust results in terms of improving the short-term severe weather forecasts. Compared to ExpRFO, ExpPFO_New better reproduces all observed supercells in the analysis field, and yields a more continuous and reasonable reflectivity distribution near the melting layer. Overall, ExpPFO_New initialized from the more accurate analysis fields produces better forecasts of reflectivity and hourly precipitation with smaller biases, especially for heavy precipitation thresholds.